Answer / Pankaj Kumar Dagar

Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The goal is to maximize a cumulative reward signal, which is feedback given by the environment for each action taken by the agent. Reinforcement learning algorithms learn through trial and error, adjusting their policy (strategy) based on the rewards received. Common reinforcement learning techniques include Q-learning, SARSA, and actor-critic methods.

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